Bibliographic record
Abstract
À partir des données des recensements canadiens de 1991 et de 1996, nous nous penchons sur la question des immigrants pauvres et à faible revenu, un sujet très peu traité dans les travaux de recherche précédents sur l'immigration. Comparativement aux Canadiens de souche, les immigrants sont constamment surreprésentés dans la classe des pauvres. Cette surreprésentation comporte une orientation ethnique et raciale claire: les immigrants appartenant aux minorités visibles vivant les pires conditions. Les modèles de régression logis‐tique révèlent que, dans leur cas, les chances d'être pauvres sont con‐sidérablement plus élevées même en tenant compte de toutes les autres variables pertinentes. Les taux de pauvreté des différentes générations d'immigrants ne suivent pas un modèle logique; ceux qui ont émigréà l'adolescence vivent dans des conditions anormales de pauvreté extrême. La comparaison entre la situation des immigrants en 1991 et en 1996 révèle que l'investissement en matière de capital humain favorise de moins en moins les immigrants. Using the 1991 and 1996 Canadian census data, the present study addresses the issue of poor or low‐income immigrants, a topic largely overlooked in previous immigration research. The authors found that, compared to native‐born Canadians, immigrants were consistently over‐represented among the poor, and that this over‐representation had a clear ethnic and racial colour, with visible minority immigrants experiencing the most severe conditions. For them, the logistic regression models show, the odds of poverty are noticeably higher, even after controlling for all other relevant variables. The poverty rates of different generations of immigrants also show an unexpected pattern, in which those who have migrated during their adolescent years experience unusually severe poverty conditions. A comparison of the situation in 1991 and 1996 shows that human capital endowments are becoming less rewarding for immigrants.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.015 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".